Homec4science
Diffusion Slepians (master) Browse

Slepi/Code/Utilities/spm12/spm_dcm_fmri_priors.m
8a186cd82567master

Last Change
Raw File

spm_dcm_fmri_priors.m
View Options

function [pE,pC,x] = spm_dcm_fmri_priors(A,B,C,D,options)
% Returns the priors for a two-state DCM for fMRI.
% FORMAT:[pE,pC,x] = spm_dcm_fmri_priors(A,B,C,D,options)
%
% options.two_state: (0 or 1) one or two states per region
% options.stochastic: (0 or 1) exogenous or endogenous fluctuations
% options.precision: log precision on connection rates
%
% INPUT:
% A,B,C,D - constraints on connections (1 - present, 0 - absent)
%
% OUTPUT:
% pE - prior expectations (connections and hemodynamic)
% pC - prior covariances (connections and hemodynamic)
% x - prior (initial) states
%__________________________________________________________________________
%
% References for state equations:
% 1. Marreiros AC, Kiebel SJ, Friston KJ. Dynamic causal modelling for
% fMRI: a two-state model.
% Neuroimage. 2008 Jan 1;39(1):269-78.
%
% 2. Stephan KE, Kasper L, Harrison LM, Daunizeau J, den Ouden HE,
% Breakspear M, Friston KJ. Nonlinear dynamic causal models for fMRI.
% Neuroimage 42:649-662, 2008.
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Karl Friston
% $Id: spm_dcm_fmri_priors.m 6198 2014-09-25 10:38:48Z karl $
% number of regions
%--------------------------------------------------------------------------
n = length(A);
% check options and D (for nonlinear coupling)
%--------------------------------------------------------------------------
try, options.stochastic; catch, options.stochastic = 0; end
try, options.induced; catch, options.induced = 0; end
try, options.two_state; catch, options.two_state = 0; end
try, options.backwards; catch, options.backwards = 0; end
try, D; catch, D = zeros(n,n,0); end
% connectivity priors and intitial states
%==========================================================================
if options.two_state
% (6) initial states
%----------------------------------------------------------------------
x = sparse(n,6);
A = logical(A - diag(diag(A)));
% precision of log-connections (two-state)
%---------------------------------------------------------------------
try, pA = exp(options.precision); catch, pA = 16; end
% prior expectations and variances
%----------------------------------------------------------------------
pE.A = (A + eye(n,n))*32 - 32;
pE.B = B*0;
pE.C = C*0;
pE.D = D*0;
% prior covariances
%----------------------------------------------------------------------
for i = 1:size(A,3)
pC.A(:,:,i) = A(:,:,i)/pA + eye(n,n)/pA;
end
pC.B = B/4;
pC.C = C*4;
pC.D = D/4;
% excitatory proportion
%----------------------------------------------------------------------
if options.backwards
pE.A(:,:,2) = A*0;
pC.A(:,:,2) = A/pA;
end
else
% one hidden state per node
%======================================================================
% (6 - 1) initial states
%----------------------------------------------------------------------
x = sparse(n,5);
% precision of connections (one-state)
%---------------------------------------------------------------------
try, pA = exp(options.precision); catch, pA = 64; end
try, dA = options.decay; catch, dA = 1; end
% prior expectations
%----------------------------------------------------------------------
if isvector(A)
A = logical(A);
pE.A = (A(:) - 1)*dA;
else
A = logical(A - diag(diag(A)));
pE.A = A/128;
end
pE.B = B*0;
pE.C = C*0;
pE.D = D*0;
% prior covariances
%----------------------------------------------------------------------
if isvector(A)
pC.A = A(:);
else
for i = 1:size(A,3)
pC.A(:,:,i) = A(:,:,i)/pA + eye(n,n)/pA;
end
end
pC.B = B;
pC.C = C;
pC.D = D;
end
% and add hemodynamic priors
%==========================================================================
pE.transit = sparse(n,1); pC.transit = sparse(n,1) + exp(-6);
pE.decay = sparse(n,1); pC.decay = sparse(n,1) + exp(-6);
pE.epsilon = sparse(1,1); pC.epsilon = sparse(1,1) + exp(-6);
% add prior on spectral density of fluctuations (amplitude and exponent)
%--------------------------------------------------------------------------
if options.induced
pE.a = sparse(2,n); pC.a = sparse(2,n) + 1/64; % neuronal fluctuations
pE.b = sparse(2,1); pC.b = sparse(2,1) + 1/64; % channel noise global
pE.c = sparse(2,n); pC.c = sparse(2,n) + 1/64; % channel noise specific
end
% prior covariance matrix
%--------------------------------------------------------------------------
pC = diag(spm_vec(pC));
return
c4science · Help